SBIR Phase I: Aerial manipulation platform for rebar tying

The broader impact/commercial potential of this SBIR Phase I project is to reduce the time required to construct concrete bridges, improve job site safety relating to rebar installation, reduce costs related to bridge construction, and improve the overall health of ironworkers. Ironworkers face some of the highest rates soft tissue damage of the industrial occupations due the constant bending over required to tie rebar. Additionally, by reducing the required construction time for bridges, communities will be able to recover from natural disasters at a faster pace. These benefits will be accomplished by automating the process of the tying rebar. This is significant due to the highly repetitive nature of the tying process, the labor shortages in the construction industry, and the fact that rebar tying often sits on the critical path of a concrete pour. To automate rebar tying, a drone platform with an integrated tie tool, specialized flight controls, and navigation system will be developed. This will take small unmanned aircraft systems (sUAS) from observation roles to a manipulation platform. Rebar tying on bridges represents a $275 million market over the next 10 years and $1.7 billion market annually across the USA for the larger general construction market. This Small Business Innovation Research (SBIR) Phase I project aims to develop a sUAS capable of tying the rebar for concrete construction on an outdoor fixture. This will represent the first commercially viable aerial manipulation system if the project is successful. A combination of computer vision, machine learning, and sensor fusion techniques will be employed to develop an autonomous system capable of allowing a drone to identify, land and tie with a high level of accuracy to enable rebar tying. Modification to the autopilot will be conducted to allow the drone to trigger the integrated rebar tool. Computer vision algorithms will be optimized and ported to run onboard the sUAS. A control system for visual servo-ing will be developed to utilize onboard computer vision algorithms and other sensors to track and land on rebar intersections accurately. High-level challenges consist of uncovering the robustness of existing rebar tool towards imprecise landing, improving landing precision, improving accuracy of rebar intersection detection, and visual servo-ing of the drone. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.